GA3 file will be the slow evolution file. The main goal will be testing to see if there is an effect caused by mutation rate or effect size when the mutational variance between two species is the same.
Prediction: Higher mutation rate will be advantages in the beginning of a simulation. A higher effect size might lead to more spatial co-evolution. Space might separate the effects.
For the initial part of this experiment I will only be running one trial per GA combination
GA combinations:
I have created some bash script to run through all the files in my data folders and add some columns and coagulate all of the files into one file per data set. In this case, there are three data sets; one for whole population data (lit), one for correlation data (cor), and one for individual grids (grid). After these all file is created, I load it into R and finish adding in some comparative columns. I also load in my figure creation functions. There originally was a problem with naming where I was putting the newt information in first (fixed, but is something to be aware of moving forward).
## All cor, lit, and grid files exist!
## This program will now end!
Information to be typed
## Group.1 x
## 1 1e-08_0.005_1e-08_0.005 -0.098163903
## 2 1e-08_0.005_1e-09_0.0158 0.016779364
## 3 1e-08_0.005_1e-10_0.05 -0.226664679
## 4 1e-08_0.005_1e-11_0.158 0.010813056
## 5 1e-09_0.0158_1e-08_0.005 -0.007287745
## 6 1e-09_0.0158_1e-09_0.0158 0.215621516
## 7 1e-09_0.0158_1e-10_0.05 -0.030173555
## 8 1e-09_0.0158_1e-11_0.158 0.250226883
## 9 1e-10_0.05_1e-08_0.005 -0.070659664
## 10 1e-10_0.05_1e-09_0.0158 -0.197977493
## 11 1e-10_0.05_1e-10_0.05 -0.102772523
## 12 1e-10_0.05_1e-11_0.158 -0.130457787
## 13 1e-11_0.158_1e-08_0.005 0.034135676
## 14 1e-11_0.158_1e-09_0.0158 -0.193835961
## 15 1e-11_0.158_1e-10_0.05 0.008829150
## 16 1e-11_0.158_1e-11_0.158 -0.145687471
explanation
more info
explanation
Info
Explanation
what did I do and why Time slices to see general trends
What does it mean?
All of the correlations across time look very similar. Generally, mean newt and snake phenotypes (red and blue) go up overtime while the local correlation between the two fluctuates. Sometime one mean phenotype stays flat. The local correlations range from being very positive to being very negative. How can the correlation start to suddenly go up and then fall? It is hard to notice any differences between the GA combinations. For space I have chosen 3 random figures.
## [1] "pattern 1e-08_0.005_1e-10_0.05_0"
## [1] "Cor between average snake pheno and local cor 0.0771531187531821"
## [1] "Cor between average newt pheno and local cor 0.420720019406375"
## [1] "Cor between average dif pheno and local cor -0.423435647379498"
## [1] "Cor between newt pheno and snake 0.292608560276865"
## [1] "pattern 1e-10_0.05_1e-10_0.05_3"
## [1] "Cor between average snake pheno and local cor -0.111179350221815"
## [1] "Cor between average newt pheno and local cor -0.392493611590906"
## [1] "Cor between average dif pheno and local cor 0.599577323888975"
## [1] "Cor between newt pheno and snake 0.870257473542623"
Explain general figures
Describe
Explain
What should I be focusing on here?
Explanations
Need to add tabs to show the mean, max, min, and population size. #3 high + cor, 0 cor, -cor
## [1] 0.239585
## [1] -0.2701397
## [1] -0.3967834
## [1] 0.5894865
## [1] 0.4039904
## [1] 0.5279311